Explanation remains a formidable challenge in AI. While today's explanation
systems are good at listing supporting evidence (“rationales”) for an answer [1]},
they rarely
explain the chain of reasoning from the evidence
to the answer, i.e., how the answer follows, given the evidence –
the goal of this work. Without this, it is hard to fully understand a system's
response and/or pinpoint the source of errors if its conclusions are wrong.
Conversely, if a system could support its answers with a chain of reasoning, new
opportunities arise for interactively teaching the machine by
debugging its mistakes.
